Performance assessment and ranking of Iranian insurance companies using an integrated model with experts preferences

Document Type : Research Paper

Authors

1 Assistant Prof., Industrial Engineering, Urmia University of Technology, Urmia, Iran

2 MSc. Student of Industrial Engineering, Urmia University of Technology, Urmia, Iran

3 BS in Industrial Engineering, Payam e Noor University of Tabriz, Tabriz, Iran

Abstract

This paper presents an integrated Data envelopment analysis (DEA) – Principal component analysis (PCA) – Analytical hierarchy process (AHP) to achieve the efficiency scores and ranks of the insurance companies. Fourteen insurance companies with thirteen input and output variables have been considered for the purpose of this study. Since the DEA model is sensitive to the number of variables in comparison to number of DMUs, to reduce data dimension, the PCA method is used. Obviously, the final ranks from PCA-DEA model is very subjective and only based on the pattern and distribution of data sets. Therefore, for incorporating the expert preferences, the AHP model is combined with two previous models and the final ranking is done by the integrated DEA-PCA-AHP and PCA-DEA model. The results of the model show that DANA, RAZI and DEY have become the best rank among insurance companies.



 

Keywords


Adler, N. & Golany, B. (2002). Including principal component weights to improve discrimination in data envelopment analysis. Journal of the Operational Research Society, 53: 985-991.
 
Alam Tabriz, A., Rajabipoor Meybodi, A. & Zareian, M. (2010). Studying the application of fuzzy topsis in improvement of efficiency measurement of bank branches using DEA, journal of industrial management, 1(3): 99-118. (In Persian)
 
Barros, C. P., Nektarios, M. & Assaf, A. (2010). Efficiency in the Greek insurance industry. European Journal of Operational Research, 205: 431-436.
 
Bian, Y. (2012). A Gram–Schmidt process-based approach for improving DEA discrimination in the presence of large dimensionality of data set. Expert Systems with Applications, 39: 3793-3799.
 
Charnes, A., Cooper, W. W. & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2: 429-444.
 
Charnes, A., Cooper, W. W., Lewin, A. Y. & Seiford, L. M. (1994). Data envelopment analysis: theory, methodology and applications. Kluwer Academic, Boston.
 
Eling, M. & Luhnen, M. (2010). Efficiency in the international insurance industry: A cross-country comparison. Journal of Banking & Finance, 34: 1497-1509.
 
Fan, L. L. (2006). Structural health monitoring base on principal components analysis implemented on a distributed and open system. Department of Building & Construction, City University of Hong Kong.
 
Ganley, J. A. & Cubbin, J. S. (1992). Public sector efficiency measurement: applications of data envelopment analysis. Elsevier Science Publishers. Amsterdam, New York.
 
Hui, Z. & Honggeng, Y. (2011) Application of weighted principal component analysis in comprehensive evaluation for power quality. IEEE, 3: 369-372.
 
Jenkins, L. & Anderson, M. (2003). A multivariate statistical approach to reducing the number of variables in data envelopment analysis. European Journal of Operational Research, 147: 51-61.
 
Kao, C. & Hwang, S. (2008). Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan. European Journal of Operational Research, 185: 418-429.
 
Khazaei, M. Izadbakhsh, H. (2009). Combination of DEA and PCA for Full Ranking of Decision Making Units, journal of industrial management, 1(2): 55-70. (In Persian)
 
Liang Liang, Yongjun Li, Shibing Li. (2009). Increasing the discriminatory power of DEA in the presence of the undesirable outputs and large dimensionality of data sets with PCA. Expert Systems with Applications, 36: 5895-5899.
 
Premachandra, I.M. (2001). A note on DEA vs. principal component analysis: An improvement to Joe Zhu’s approach. European Journal of Operational Research, 132: 553-560.
 
Saaty, T. L. (1980). The analytic hierarchy process. McGraw- Hill. New York.
 
Saaty, T. L. (1985). Decision making for leaders. Belmont, Life Time Leaning Publications. California.
 
Saaty, T. L. (1990). How to make a decision: the analytic hierarchy process. European Journal of Operational Research, 48: 9-26.
 
Scheel, H. (2001). Undesirable outputs in efficiency valuations. European Journal of Operational Research, 132: 400-410.
 
Seiford, L.M. & Zhu, J. (2002). Modeling undesirable factors in efficiency evaluation. European Journal of Operational Research, 142: 16-20.
 
Shahriari, S. Razavi, S. Asgharizadeh, E (2013). Fuzzy data envelopment analysis and a new approach FIEP/AHP for full ranking of decision making units: A case study of humanities faculty of Tehran University, journal of industrial management, 5(1): 21-42. (In Persian)
 
Shanmugam, R. & Johnson, C. (2007). At a crossroad of data envelopment and principal component analyses. Omega, 35: 351-364.
 
Yao, S., Han, Z. & Feng, G. (2007). On technical efficiency of China's insurance industry after WTO accession. China Economic Review, 18: 66-86.
 
Zhu, J. (1998). Data envelopment analysis vs. principal component analysis: An illustrative study of economic performance of Chinese cities. European Journal of Operational Research, 111: 50-61.